增加非流型点loss处理

brep2sdf/config/default_config.py

@@ -49,7 +49,7 @@ class TrainConfig:
     # 基本训练参数
     batch_size: int = 8
     num_workers: int = 4
-    num_epochs: int = 1000
+    num_epochs: int = 100
     learning_rate: float = 0.1
     min_lr: float = 1e-5
     weight_decay: float = 0.01

brep2sdf/networks/decoder.py

@@ -108,19 +108,16 @@ class Decoder(nn.Module):
         '''
         # 直接使用输入的特征矩阵，因为形状已经是 (S, D)
         x = feature_matrix
-        logger.debug(f"decoder-x:{x}")
 
         for layer, lin in enumerate(self.sdf_modules):
             if layer in self.skip_in:
                 x = torch.cat([x, x], -1) / torch.sqrt(torch.tensor(2.0))  # 使用 torch.sqrt
 
             x = lin(x)
-            logger.debug(f"decoder-x-lin:{x}")
             if layer < self.sdf_layers - 2:
                 x = self.activation(x)
         
         output_value = x  # 所有 f 的值
-        logger.debug(f"decoder-output:{output_value}")
         # 调整输出形状为 (S)
         f = output_value.squeeze(-1)
 

brep2sdf/networks/encoder.py

@@ -105,7 +105,6 @@ class Encoder(nn.Module):
         for idx, volume in enumerate(self.feature_volumes):
             if idx == patch_id:
                 patch_features = volume.forward(surf_points)
-                break
         
         # 获取背景场特征
         background_features = self.background.forward(surf_points)

brep2sdf/networks/loss.py

@@ -111,10 +111,13 @@ class LossManager:
         return correction_loss
 
 
-    def compute_loss(self, points,
+    def compute_loss(self, 
+                mnfld_pnts,
+                nonmnfld_pnts,
                 normals,
                 gt_sdfs,
-                pred_sdfs):
+                mnfld_pred,
+                nonmnfld_pred):
         """
         计算流型损失的逻辑
 
@@ -123,20 +126,34 @@ class LossManager:
         """
         # 强制类型转换确保一致性
         normals = normals.to(torch.float32)
-        pred_sdfs = pred_sdfs.to(torch.float32)
+        mnfld_pred = mnfld_pred.to(torch.float32)
         gt_sdfs = gt_sdfs.to(torch.float32)
     
         # 计算流形损失 
-        manifold_loss = self.position_loss(pred_sdfs, gt_sdfs)
+        manifold_loss = self.position_loss(mnfld_pred, gt_sdfs)
         
         # 计算法线损失
-        normals_loss = self.normals_loss(normals, points, pred_sdfs)
+        normals_loss = self.normals_loss(normals, mnfld_pnts, mnfld_pred)
         #logger.gpu_memory_stats("计算法线损失后")
 
+        # 计算Eikonal损失
+        eikonal_loss = self.eikonal_loss(nonmnfld_pnts, nonmnfld_pred)
+
+        # 计算离表面损失
+        offsurface_loss = self.offsurface_loss(nonmnfld_pnts, nonmnfld_pred)
+
+        # 计算一致性损失
+        #onsistency_loss = self.consistency_loss(mnfld_pnts, mnfld_pred, all_fi)
+
+        # 计算修正损失
+        #correction_loss = self.correction_loss(mnfld_pnts, mnfld_pred, all_fi)
+
         # 汇总损失
         loss_details = {
             "manifold": self.weights["manifold"] * manifold_loss,
             "normals": self.weights["normals"] * normals_loss,
+            "eikonal": self.weights["eikonal"] * eikonal_loss,
+            "offsurface": self.weights["offsurface"] * offsurface_loss
         }
         
         # 计算总损失

brep2sdf/networks/network.py

@@ -136,7 +136,6 @@ class Net(nn.Module):
         surf_points (P, S): 
         return (P, S)
         """
-        logger.debug(surf_points)
         feature_mat = self.encoder.forward_training_volumes(surf_points,patch_id)
         f_i = self.decoder.forward_training_volumes(feature_mat)
         return f_i.squeeze()

brep2sdf/networks/sample.py

@@ -0,0 +1,22 @@
+import torch
+
+
+class NormalPerPoint():
+
+    def __init__(self, global_sigma, local_sigma=0.01):
+        self.global_sigma = global_sigma
+        self.local_sigma = local_sigma
+
+    def get_points(self, pc_input, local_sigma=None):
+        batch_size, sample_size, dim = pc_input.shape
+
+        if local_sigma is not None:
+            sample_local = pc_input + (torch.randn_like(pc_input) * local_sigma.unsqueeze(-1))
+        else:
+            sample_local = pc_input + (torch.randn_like(pc_input) * self.local_sigma)
+
+        sample_global = (torch.rand(batch_size, sample_size // 8, dim, device=pc_input.device) * (self.global_sigma * 2)) - self.global_sigma
+
+        sample = torch.cat([sample_local, sample_global], dim=1)
+
+        return sample

brep2sdf/train.py

@@ -12,6 +12,7 @@ from brep2sdf.networks.network import Net
 from brep2sdf.networks.octree import OctreeNode
 from brep2sdf.networks.loss import LossManager
 from brep2sdf.networks.patch_graph import PatchGraph
+from brep2sdf.networks.sample import NormalPerPoint
 from brep2sdf.utils.logger import logger
 
 
@@ -142,6 +143,11 @@ class Trainer:
         self.loss_manager = LossManager(ablation="none")
         logger.gpu_memory_stats("训练器初始化后")
 
+        self.sampler = NormalPerPoint(
+            global_sigma=0.1,  # 全局采样标准差
+            local_sigma=0.01  # 局部采样标准差
+        )
+
         logger.info(f"初始化完成，正在处理模型 {self.model_name}")
 
     
@@ -200,7 +206,9 @@ class Trainer:
         total_loss = 0.0
         total_loss_details = {
             "manifold": 0.0,
-            "normals": 0.0
+            "normals": 0.0,
+            "eikonal": 0.0,
+            "offsurface": 0.0
         }
         accumulated_loss = 0.0  # 新增：用于累积多个step的loss
         
@@ -208,19 +216,21 @@ class Trainer:
         self.optimizer.zero_grad()
         
         for step, surf_points in enumerate(self.data['surf_ncs']):
-            points = torch.tensor(surf_points, device=self.device)
-            gt_sdf = torch.zeros(points.shape[0], device=self.device)
+            mnfld_points = torch.tensor(surf_points, device=self.device)
+            nonmnfld_pnts = self.sampler.get_points(mnfld_points.unsqueeze(0)).squeeze(0)  # 生成非流形点
+            gt_sdf = torch.zeros(mnfld_points.shape[0], device=self.device)
             normals = None
             if args.use_normal:
                 normals = torch.tensor(self.data["surf_pnt_normals"][step], device=self.device)
 
             # --- 准备模型输入，启用梯度 ---
-            points.requires_grad_(True)  # 在检查之后启用梯度
+            mnfld_points.requires_grad_(True)  # 在检查之后启用梯度
+            nonmnfld_pnts.requires_grad_(True)  # 在检查之后启用梯度
 
             # --- 前向传播 ---
             self.optimizer.zero_grad()
-            pred_sdf = self.model.forward_training_volumes(points, step)
-            logger.debug(f"pred_sdf:{pred_sdf}")
+            mnfld_pred = self.model.forward_training_volumes(mnfld_points, step)
+            nonmnfld_pred = self.model.forward_training_volumes(nonmnfld_pnts, step)
 
             if self.debug_mode:
                 # --- 检查前向传播的输出 ---
@@ -230,10 +240,12 @@ class Trainer:
             try:
                 if args.use_normal:
                     loss, loss_details = self.loss_manager.compute_loss(
-                        points,
+                        mnfld_points,
+                        nonmnfld_pnts,
                         normals,
                         gt_sdf,
-                        pred_sdf
+                        mnfld_pred,
+                        nonmnfld_pred
                     )
                 else:
                     loss = torch.nn.functional.mse_loss(pred_sdf, gt_sdf)
@@ -278,6 +290,7 @@ class Trainer:
         return total_loss  # 返回平均损失而非累计值
     
 
+
     def train_epoch(self, epoch: int) -> float:
         # --- 1. 检查输入数据 ---
         # 注意：假设 self.sdf_data 包含 [x, y, z, nx, ny, nz, sdf] (7列) 或 [x, y, z, sdf] (4列)